# LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications

arXiv: https://arxiv.org/abs/2401.17029 · HTML: https://mlsanity.com/paper/2401.17029/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 70 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Neural network reconstruction of cosmology using the Pantheon compilation](https://mlsanity.com/paper/2305.15499/) (2023)
- [Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning](https://mlsanity.com/paper/2303.05169/) (2023)

## Built on by (most cited first, 4 of 4)

- [A possible late-time transition of MB inferred via neural networks](https://mlsanity.com/paper/2402.10502/) (2024)
- [Model-independent cosmological inference post DESI DR1 BAO measurements](https://mlsanity.com/paper/2405.19178/) (2024)
- [Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions](https://mlsanity.com/paper/2412.14750/) (2024)
- [What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?](https://mlsanity.com/paper/2407.19481/) (2024)

## Abstract (arXiv)

We investigate the prospect of reconstructing the ''cosmic distance ladder'' of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernovae compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other datasets like baryon acoustic oscillations, calibration of high-redshift datasets such as gamma ray bursts, and use as a model-independent mock catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.